Road Friction Coefficient Research Articles

Linearized Recursive Least Squares Methods for Real-Time Identification of Tire–Road Friction Coefficient

The tire-road friction coefficient is critical information for conventional vehicle safety control systems. Most previous studies on tire-road friction estimation have only considered either longitudinal or lateral vehicle dynamics, which tends to cause significant underestimation of the actual tire-road friction coefficient. In this paper, the parameters, including the tire-road friction coefficient, of the combined longitudinal and lateral brushed tire model are identified by linearized recursive least squares (LRLS) methods, which efficiently utilize measurements related to both vehicle lateral and longitudinal dynamics in real time. The simulation study indicates that by using the estimated vehicle states and the tire forces of the four wheels, the suggested algorithm not only quickly identifies the tire-road friction coefficient with great accuracy and robustness before tires reach their frictional limits but successfully estimates the two different tire-road friction coefficients of the two sides of a vehicle on a split- μ surface as well. The developed algorithm was verified through vehicle dynamics software Carsim and MATLAB/Simulink.

Simulation Analyzed of Vehicle Operating Safety in Rain Weather Condition Based on ADAMS

The safety of vehicle operating has tight relation with the road condition; vehicle operating safety accident is easy occurred in badness road condition. This paper studied the vehicle Operating Safety in rain weather condition based on Multi-Rigid Body system Automatic Dynamic Analysis of Mechanical Systems (ADAMS). The models include Vehicle model, Road model, Vehicle and Road coupling model, Simulation module were set up. By changing road friction coefficient, road conditions in sunny day, dry, and rain weather were simulated. Single lane change text and ramp steering text were carried out, and the response output of lateral displacement was obtained. Computation result indicated that in the single lane change when road friction coefficient in rain weather is 0.4, vehicle speed is 60km/h in the single line simulation; the vehicle will be easily out of control. When vehicle speed is 55 km/h, maximum steer value is 70degree in the single lane simulation, vehicle will go haywire. In the ramp steer simulation when vehicle speed is 40 km/h, vehicle will go haywire.

A Study on In-wheel Motor Control to Improve Vehicle Stability Using Human-in-the-Loop Simulation

In this study, an integrated motor control algorithm for an in-wheel electric vehicle is suggested. It consists of slip control that controls the in-wheel motor torque using the road friction coefficient and slip ratio; yaw rate control that controls the in-wheel motor torque according to the road friction coefficient and the yaw rate error; and velocity control that controls the vehicle velocity by a weight factor based on the road friction coefficient and the yaw rate error. A co-simulator was developed, which combined the vehicle performance simulator based on MATLAB/Simulink and the vehicle model of CarSim. Based on the co-simulator, a human-in-the-loop simulation environment was constructed, in which a driver can directly control the steering wheel, the accelerator pedal, and the brake pedal in real time. The performance of the integrated motor control algorithm for the in-wheel electric vehicle was evaluated through human-in-the-loop simulations.

Development of a Tactile Sensor to Measure Tire Friction Coefficients in Arbitrary Directions

In order to develop intelligent tires that measures road surface friction coefficients, the present study proposes a simple tactile sensor that measures threedimensional loads. The sensor is composed of a cantilever called a whisker that is fixed to a base. The base is an elastic plate that has three strain gauges attached to its surface equiangularly around the fixed point of the whisker. The whisker is covered in cylindrical rubber, and the bottom surface is used as a contact. When the sensor touches a surface with the contact and traces it, vertical and horizontal loads are applied to the contact, compressing and bending the whisker so that the base is deformed elastically. Strains induced on the base by this deformation are measured by strain gauges. The study proposes a method obtaining values of vertical load, horizontal load and its direction from measured strains. Accordingly, the proposed sensor measures threedimensional load and obtains the contact surface friction coefficient. In the study, we fabricate a tactile sensor prototype and show its feasibility.

Co-Simulation of Composite ABS Control for In-Wheel Motor Drive Electric Vehicle Based on Threshold Control Algorithm

According to the brake characteristics of in-wheel motor drive electric vehicles, and basing on threshold control method, we describe one kind of composite ABS control theory about electric motor ABS combined with hydraulic friction ABS, and establish a co-simulation vehicle model. The composite ABS control method is a control method that the electric motor ABS control works together with the hydraulic ABS control. Both of the two modes of ABS control logic were using logic threshold control method. The model of the in-wheel motor drive electric vehicle was established with AMESim, and the model of the composite ABS controller was built with Simulink. The control performance of composite ABS in different braking strength and different road friction coefficients is simulated. Co-simulation was carried out. Through analysis, a number of parameters curves were obtained. It proves that the composite ABS control method for in-wheel motor drive electric vehicles can effectively control the slip rate, and ensure braking stability.

Tyre–road friction coefficient estimation based on tyre sensors and lateral tyre deflection: modelling, simulations and experiments

The estimation of the tyre–road friction coefficient is fundamental for vehicle control systems. Tyre sensors enable the friction coefficient estimation based on signals extracted directly from tyres. This paper presents a tyre–road friction coefficient estimation algorithm based on tyre lateral deflection obtained from lateral acceleration. The lateral acceleration is measured by wireless three-dimensional accelerometers embedded inside the tyres. The proposed algorithm first determines the contact patch using a radial acceleration profile. Then, the portion of the lateral acceleration profile, only inside the tyre–road contact patch, is used to estimate the friction coefficient through a tyre brush model and a simple tyre model. The proposed strategy accounts for orientation-variation of accelerometer body frame during tyre rotation. The effectiveness and performance of the algorithm are demonstrated through finite element model simulations and experimental tests with small tyre slip angles on different road surface conditions.

Comparison of Braking Performance by Electro-Hydraulic ABS and Motor Torque Control for In-wheel Electric Vehicle

In this study, a slip control algorithm using motor torque control during the braking of the in-wheel electric vehicle was proposed and an anti-lock brake system (ABS) simulator was developed on the basis of the test results for electro-hydraulic ABS. The slip control algorithm using motor torque control limits the in-wheel motor torque according to road friction coefficient and slip ratio while the ABS prevents locking of the wheel by lowering or raising the brake pressure through solenoid valve control. A MATLAB/Simulink model and a CarSim vehicle model were developed, and the motor torque control and ABS simulator were applied to the MATLAB/Simulink-CarSim co-simulator and compared braking performance.

Dual extended Kalman filter for combined estimation of vehicle state and road friction

Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute(HSRI) tire model; the dual extended Kalman filter (DEKF) technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions(high-friction, low-friction, and joint-friction). On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.

An Intelligent Tire Based Tire-Road Friction Estimation Technique and Adaptive Wheel Slip Controller for Antilock Brake System

The contact between the tire and the road is the key enabler of vehicle acceleration, deceleration and steering. However, due to changes to the road conditions, the driver's ability to maintain a stable vehicle may be at risk. In many cases, this requires intervention from the chassis control systems onboard the vehicle. Although these systems perform well in a variety of situations, their performance can be improved if a real-time estimate of the tire-road friction coefficient is available. Existing tire-road friction estimation approaches often require certain levels of vehicle longitudinal and/or lateral motion to satisfy the persistence of excitation condition for reliable estimations. Such excitations may undesirably interfere with vehicle motion controls. This paper presents a novel development and implementation of a real-time tire-road contact parameter estimation methodology using acceleration signals from an intelligent tire. The proposed method characterizes the terrain using the measured frequency response of the tire vibrations and provides the capability to estimate the tire road friction coefficient under extremely lower levels of force utilization. Under higher levels of force excitation (high slip conditions), the increased vibration levels due to the stick/slip phenomenon linked to the tread block vibration modes make the proposed tire vibrations based method unsuitable. Therefore for high slip conditions, a brush model-based nonlinear least squares (NLLS) parameter estimation approach is proposed. Hence an integrated approach using the intelligent tire based friction estimator and the model based estimator gives us the capability to reliably estimate friction for a wider range of excitations. Considering the strong interdependence between the operating road surface condition and the instantaneous forces and moments generated; this real time estimate of the tire-road friction coefficient is expected to play a pivotal role in improving the performance of a number of vehicle control systems. In particular, this paper focuses on the possibility of enhancing the performance of the ABS control systems. In order to achieve the aforementioned objectives, the design and implementation of a fuzzy/sliding mode/proportional integral (fuzzy-SMC-PI (FSP)) control methodology is proposed. The results show significant improvements in the stopping distance of a vehicle equipped with an intelligent tire based FSP controller as compared to a vehicle equipped with a standard ABS.

Testing Technology of Dynamic Road Friction Coefficient

The dynamic road adhesion coefficients at 30km/h and 50km/h for dry condition were measured through the real vehicle equipped with a test system driving on the asphalt dry road. The test system included the fifth wheel instrument, the wheel speed sensors, the programmed strain amplifier and ACME portable industrial personal computer. The results show that longitudinal peak adhesion coefficients at 30km/h and 50km/h for the dry condition are about 0.7. Longitudinal adhesion coefficient increases rapidly with the slip rate increasing and soon reaches the maximum. The longitudinal peak adhesion coefficient occures when the slip rate is about 12%.

Design of traction control system for electric scooter using self-organising fuzzy control

A Traction Control System (TCS) using Self-Organising Fuzzy Control (SOFC) is proposed for electric scooter in this paper. Vertical load and traction force observers are used to estimate road friction coefficient. Road condition observer is used to estimate the gradient of road friction coefficient with respect to tyre slip ratio. Reference slip ratio for TCS is determined using estimated gradient. According to the error and error change of slip ratio, SOFC adjusts the torque demand for wheel motor. Simulation and experimental results show that the proposed TCS can detect road friction change and maintain slip ratio in the stable region.

Review on Vehicle Sideslip Angle Estimation

Vehicle sideslip angle is an important state variable in vehicle stability control system. The sideslip angle estimation problem can be divided into three aspects: Lateral velocity estimation, longitudinal velocity estimation and model parameter adaptive estimation. Then the three aspects are reviewed respectively. The advantages and disadvantages of kinematic estimation method and dynamic estimation method are compared and analysed, leading to the viewpoint that the fusion multiple estimation methods has obvious benefits and is more suitable for sideslip angle estimation. Furthermore, the performances of the typical observer techniques applied to sideslip angle estimation are compared and analysed. From analysis, it is pointed out that model parameter adaptive estimation is an effective method to improve the accuracy of sideslip angle estimation. Therefore, the parameter adaptive estimation methods that are adaptive to changes of road peak friction coefficient, tire sideslip stiffness, road slope and bank angle etc. are introduced respectively. Through analysis of the new structural characteristics of the distributed electric drive vehicle, the impacts on sideslip angle estimation issues are summarized. Then, it is pointed out that the full use of in-wheel motor torque information is one of the most important measures to improve the sideslip angle estimation performance. Based upon distributed electric drive vehicle, the key technologies and advantages of each state and parameter estimation aspects are discussed in details.

Slip-Slope Estimation of Mutative Road Friction Coefficient Based on Unscented Particle Filter

The accurate information of road friction coefficient allows the control algorithm in vehicle activity system to adapt to external driving conditions. This paper developed a slip-slope friction coefficient estimation method based on Unscented Particle Filter. A 7-DOF non-linear vehicle dynamic model was established. The normal force of tire was approximately calculated from the vehicle dynamic model; the slip and longitudinal force of tire were estimated through a combination of tire mechanical model and UPF(Unscented Particle Filter) method. Finally slip-slopes of different adhesion level roads was obtained. Through virtual test environment in ADAMS/Car, the estimation method proposed was verified to be effective and reliable under various road condition. From the method the relationships between the slip-slope and road friction coefficient are achieved.

An ABS control logic based on wheel force measurement

The paper presents an anti-lock braking system (ABS) control logic based on the measurement of the longitudinal forces at the hub bearings. The availability of force information allows to design a logic that does not rely on the estimation of the tyre–road friction coefficient, since it continuously tries to exploit the maximum longitudinal tyre force. The logic is designed by means of computer simulation and then tested on a specific hardware in the loop test bench: the experimental results confirm that measured wheel force can lead to a significant improvement of the ABS performances in terms of stopping distance also in the presence of road with variable friction coefficient.

Vehicle Dynamic Prediction Systems with On-Line Identification of Vehicle Parameters and Road Conditions

This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event.

Tire–road friction coefficient and tire cornering stiffness estimation based on longitudinal tire force difference generation

A sequential tire cornering stiffness coefficient and tire–road friction coefficient (TRFC) estimation method is proposed for some advanced vehicle architectures, such as the four-wheel independently-actuated (FWIA) electric vehicles, where longitudinal tire force difference between the left and right sides of the vehicle can be easily generated. Such a tire force difference can affect the vehicle yaw motion, and can be utilized to estimate the tire cornering stiffness coefficient and TRFC. The proposed tire cornering stiffness coefficient and TRFC identification method has the potential of estimating these parameters without affecting the vehicle desired motion control and trajectory tracking objectives. Simulation and experimental results with a FWIA electric vehicle show the effectiveness of the proposed estimation method.

Enhancement of Collision Mitigation Braking System Performance Through Real-Time Estimation of Tire-road Friction Coefficient by Means of Smart Tires

In the case of modern day vehicle control systems employing a feedback control structure, a real-time estimate of the tire-road contact parameters is invaluable for enhancing the performance of the chassis control systems such as anti-lock braking systems (ABS) and electronic stability control (ESC) systems. However, at present, the commercially available tire monitoring systems are not equipped to sense and transmit high speed dynamic variables used for real-time active safety control systems. Consequently, under the circumstances of sudden changes to the road conditions, the driver's ability to maintain control of the vehicle maybe at risk. In many cases, this requires intervention from the chassis control systems onboard the vehicle. Although these systems perform well in a variety of situations, their performance can be improved if a real-time estimate of the tire-road friction coefficient is available. Existing tire-road friction estimation approaches often require certain levels of vehicle longitudinal and/or lateral motion to satisfy the persistence of excitation condition for reliable estimations. Such excitations may undesirably interfere with vehicle motion controls. This paper presents a novel development and implementation of a real-time tire-road contact parameter estimation methodology using acceleration signals from a smart/intelligent tire. The proposed method characterizes the terrain using the measured frequency response of the tire vibrations and provides the capability to estimate the tire road friction coefficient under extremely lower levels of force utilization. Under higher levels of force excitation (high slip conditions), the increased vibration levels due to the stick/slip phenomenon linked to the tread block vibration modes make the proposed tire vibrations based method unsuitable. Therefore for high slip conditions, a tire-road friction model-based parameter estimation approach is proposed. Hence an integrated approach using the smart/intelligent tire based friction estimator and the model based estimator gives us the capability to reliably estimate friction for a wider range of excitations. Considering the strong interdependence between the operating road surface condition and the instantaneous forces and moments generated; this real time estimate of the tire-road friction coefficient is expected to play a pivotal role in improving the performance of a number of vehicle control systems. In particular, this paper focuses on the possibility of enhancing the performance of collision mitigation braking systems. Language: en

Simultaneous estimation of road friction and sideslip angle based on switched multiple non-linear observers

In this study the author develop a practically stable switched non-linear observer bank for simultaneously estimating the vehicle sideslip angle and the road friction coefficient. Each individual non-linear state estimator in the observer bank is based on a non-linear lateral dynamics vehicle model that is parametrised with a distinct road friction coefficient. The inputs to the non-linear observers are typical signals that are available within lateral stability control systems, which include the vehicle speed, steer angle, lateral acceleration and the yaw rate. The authors show that the suggested non-linear state estimator is practically stable under arbitrary switching. Finally, the authors provide numerical simulations to demonstrate the efficacy of our switched non-linear observer design technique.

Model predictive regenerative braking control for lightweight electric vehicles with in-wheel motors

This paper presents a nonlinear model predictive controller for regenerative braking control of lightweight electric vehicles equipped with in-wheel motors. In-wheel-motors-driven electric vehicles possess significant advantages such as actuation flexibilities, torque control precision, and energy recovery improvement by direct regenerative braking control. The proposed controller not only improves the regenerative braking energy recovery by determining the front and rear braking torques independently but also prevents wheel locks during deceleration when the tire–road friction coefficient is low. The energy-saving objective is accomplished by including in the cost function the additional penalty term on the motor-to-battery regenerative braking power, while the safety objective is formulated as hard constraints on the longitudinal slip ratios of the wheels. Since the problem is based on a nonlinear vehicle longitudinal model, the global minimum within each time step is searched for by gridding the initial torque plane. Simulation results, based on a vehicle model in CarSim®, show that the proposed nonlinear model predictive controller is capable of restoring considerably more regenerative braking energy than a conventional proportional–integral controller supplemented with a feedforward control effort and another nonlinear model predictive controller with no consideration of the energy recovery and of maintaining a good vehicle-speed-tracking performance.

Estimation of brake pressure and tyre-road friction during ABS activation

Anti-lock Braking System (ABS) control algorithm and diagnostic function rely heavily on information of road friction coefficients and brake cylinder pressure. To provide a more efficient and reliable means of friction and pressure estimation, an algorithm based on wheel speed signals and control commands for hydraulic valve operation is proposed. Estimated results are achieved by comparing the sums of squared speed errors between the measured wheel speeds and those based on a brake pressure model and wheel dynamics. Performance of the algorithm is evaluated using ABS test data under various braking conditions.